Reliable computational quantification of liver fibrosis is compromised by inherent staining variation

Abstract

Abstract Biopsy remains the gold‐standard measure for staging liver disease, both to inform prognosis and to assess the response to a given treatment. Semiquantitative scores such as the Ishak fibrosis score are used for evaluation. These scores are utilised in clinical trials, with the US Food and Drug Administration mandating particular scores as inclusion criteria for participants and using the change in score as evidence of treatment efficacy. There is an urgent need for improved, quantitative assessment of liver biopsies to detect small incremental changes in liver architecture over the course of a clinical trial. Artificial intelligence (AI) methods have been proposed as a way to increase the amount of information extracted from a biopsy and to potentially remove bias introduced by manual scoring. We have trained and evaluated an AI tool for measuring the amount of scarring in sections of picrosirius red‐stained liver. The AI methodology was compared with both manual scoring and widely available colour space thresholding. Four sequential sections from each case were stained on two separate occasions by two independent clinical laboratories using routine protocols to study the effect of inter‐ and intra‐laboratory staining variation on these tools. Finally, we compared these methods to second harmonic generation (SHG) imaging, a stain‐free quantitative measure of collagen. Although AI methods provided a modest improvement over simpler computer‐assisted measures, staining variation both within and between laboratories had a dramatic effect on quantitation, with manual assignment of scar proportion being the most consistent. Manual assessment also most strongly correlated with collagen measured by SHG. In conclusion, results suggest that computational measures of liver scarring from stained sections are compromised by inter‐ and intra‐laboratory staining. Stain‐free quantitative measurement using SHG avoids staining‐related variation and may prove more accurate in detecting small changes in scarring that may occur in therapeutic trials.

Keywords

• liver fibrosis
• histological scoring
• artificial intelligence
• digital pathology